Assessment of Dairy Cow Welfare through Predictive Modelling of Individual and Social Behaviour

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The aim of the project is to develop an automated system that uses behavioural observations to predict the onset of diseases such as lameness and mastitis within a commercial dairy herd. The project is highly timely: the latest automated wireless sensors provided by our project partner will allow us to collect an unprecedented amount of data on the behaviour of large herds of cows (140+ individuals) over months at a time.

With an automated system the true behavioural states of each individual cow are not known but instead an observed output state is determined from the positional and activity data collected automatically using the wireless sensors. This is an ideal scenario to use Hidden Markov Models (HMM). HMMs are a flexible statistical tool that allow one to model and analyse sequences of behaviour and have not previously been used in this context. In particular we will use HMMs to identify atypical cow behaviour and we hypothesise that this will be a useful predictor of disease state.

Social Network Analysis (SNA) is a powerful framework which provides metrics that quantify social structure at different levels of organisation. These metrics can be used to test hypotheses regarding the relationship between an individual's social network position and its attributes such as disease status. A highly novel aspect of this project will be to use SNA of the behavioural data to make predictions about changes in welfare of individual cows within the herd.

Using computer learning techniques such as artificial neural networks we will combine the results of the HMM and SNA analysis together with other indicators of welfare to predict disease onset in individual cows. This will lead to the development of an on-farm automated 'early warning' system for disease detection. We will also apply the methodology to try to predict two other important aspects of cow welfare: the onset of oestrus and the time of calving.

The monitoring of health and welfare of dairy cows has important economic and societal impacts. The UK dairy industry is in the top 10 of the world's producing nations with an annual turnover of around £6 billion and employs approximately 84,000 people in the UK. This research will provide the UK dairy industry with advice on how automated monitoring of individual and social behaviour can be used for early identification of important diseases of dairy cows in order to promote best practice management for their health. This will contribute directly to the welfare and production of dairy cows in an industry where there are currently high levels of disease, with associated sustainability issues and public concerns for animal wellbeing.

Veterinary and animal science research has had a longstanding partnership between academia and industry. The main beneficiaries for this research are the 15,000 dairy farms in the UK with their 1.8 million cows. Disease is a common occurrence in dairy cows with 15-39 per cent of cows suffering from lameness (with up to 79 per cent of cows on a single farm) with an individual case costing an estimated £240. A direct end-user for the research findings will be our industry collaborator DairyCo who represent the UK dairy industry and will disseminate findings through their 'Cow Signals' programme, a national knowledge transfer initiative that directly engages and informs farmers about the importance of animal behaviour in welfare monitoring. We will also engage directly with producers through our collaboration with Milk Link who are owned by over 1500 UK dairy farmers. Milk Link employs over 1200 people at eight processing and packaging facilities in the UK and has an annual turnover of £586 million (2010/11).

This research will also have significant international impact, as exemplified by the fact that lameness and mastitis have been highlighted as the most important factors in a survey on the consequences of poor welfare in cows by the European Food Safety Authority. The results of our research will be relevant to all high producing nations maintaining intensive dairy units.

Better systems for understanding the link between behaviour and welfare have the potential to have significant impact on other sectors of the food production industry that rear animals in social groups (e.g. beef cattle, sheep and pigs). Our research will also be of significant interest to those seeking to have more informed welfare standards including the leading supermarket chains and animal welfare charities. The general public are increasingly interested in the welfare of farm animals as evidenced by the popularity of food produced through Farm Assurance schemes. The research will have a significant impact on our project partner, Omnisense Limited, enabling them to develop and test novel technology that could be used for a commercial 'early warning system' for on-farm disease detection.